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| Criterion |  Knowledge Modeling Shape-First Modeling |  Decision Making Decision Matrix |  Growth A/B Testing |  Product Discovery Smoke Test |
|---|---|---|---|---|
Purposedifferent | Shape-First Modeling defines data quality through shapes before implementation or integration frays at the edges. The approach fits when validation and data contracts should be part of the design from the start. | When several options collide with several criteria, comparison quickly becomes subjective. A Decision Matrix makes the trade-off visible and brings weighting, criteria, and options into a shared logic. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. |
Complexitydifferent | Medium | Medium | High | Low |
Timedifferent | Halber Tag pro Domain Slice | 45-90 min | 1-4 Wochen | 1-5 Tage |
Participantsdifferent | 1-4 | 2-8 | 1-6 | Nutzertraffic |
Formatdifferent | Workshop + async | Workshop + async | Async | Async |
Outputdifferent | SHACL or ShEx Shapes, Validation Reports, Data Contracts, Shape Documentation | Decision Matrix, Scoring Rationale, Selected Option | Experiment results, Decision log, Learning summary | Interest Metrics, Conversion Signal, Learning Note |
Tagsno overlap | Knowledge graphValidationSemantic | DecisionCriteriaScoringTradeoffs | ExperimentsGrowthAnalyticsValidation | ValidationExperimentsDemandGrowth |